Efficient calculation of similarity search values and digest block boundaries for data deduplication

ABSTRACT

For efficient calculation of both similarity search values and boundaries of digest blocks in data deduplication, input data is partitioned into chunks, and for each chunk a set of rolling hash values is calculated. A single linear scan of the rolling hash values is used to produce both similarity search values and boundaries of the digest blocks of the chunk. The rolling hash values are used to contribute to the calculation of the similarity search values and to the calculation of the boundaries of the digest blocks.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of U.S. patent application Ser. No.13/840,094, filed on Mar. 15, 2013, now U.S. Pat. No. 9,244,937.

BACKGROUND OF THE INVENTION

Field of the Invention

The present invention relates in general to computers, and moreparticularly to an efficient calculation of both similarity searchvalues and boundaries of digest blocks in a data deduplication system ina computing environment.

Description of the Related Art

In today's society, computer systems are commonplace. Computer systemsmay be found in the workplace, at home, or at school. Computer systemsmay include data storage systems, or disk storage systems, to processand store data. Large amounts of data have to be processed daily and thecurrent trend suggests that these amounts will continue beingever-increasing in the foreseeable future. An efficient way to alleviatethe problem is by using deduplication. The idea underlying adeduplication system is to exploit the fact that large parts of theavailable data are copied again and again, by locating repeated data andstoring only its first occurrence. Subsequent copies are replaced withpointers to the stored occurrence, which significantly reduces thestorage requirements if the data is indeed repetitive.

SUMMARY OF THE DESCRIBED EMBODIMENTS

In one embodiment, a method is provided for efficient calculation ofboth similarity search values and boundaries of digest blocks in a datadeduplication system using a processor device in a computingenvironment. In one embodiment, by way of example only, input data ispartitioned into data chunks, and for each chunk a set of rolling hashvalues is calculated. A single linear scan of the rolling hash values isused to produce both similarity search values and boundaries of thedigest blocks of the chunk. The rolling hash values are used tocontribute to the calculation of the similarity search values and to thecalculation of the boundaries of the digest blocks.

In addition to the foregoing exemplary method embodiment, otherexemplary system and computer product embodiments are provided andsupply related advantages. The foregoing summary has been provided tointroduce a selection of concepts in a simplified form that are furtherdescribed below in the Detailed Description. This Summary is notintended to identify key features or essential features of the claimedsubject matter, nor is it intended to be used as an aid in determiningthe scope of the claimed subject matter. The claimed subject matter isnot limited to implementations that solve any or all disadvantages notedin the background.

BRIEF DESCRIPTION OF THE DRAWINGS

In order that the advantages of the invention will be readilyunderstood, a more particular description of the invention brieflydescribed above will be rendered by reference to specific embodimentsthat are illustrated in the appended drawings. Understanding that thesedrawings depict embodiments of the invention and are not therefore to beconsidered to be limiting of its scope, the invention will be describedand explained with additional specificity and detail through the use ofthe accompanying drawings, in which:

FIG. 1 is a block diagram illustrating a computing system environmenthaving an example storage device in which aspects of the presentinvention may be realized;

FIG. 2 is a block diagram illustrating a hardware structure of datastorage system in a computer system in which aspects of the presentinvention may be realized;

FIG. 3 is a flowchart illustrating an exemplary method for digestretrieval based on similarity search in deduplication processing in adata deduplication system in which aspects of the present invention maybe realized;

FIG. 4 is a flowchart illustrating an exemplary alternative method fordigest retrieval based on similarity search in deduplication processingin a data deduplication system in which aspects of the present inventionmay be realized; and

FIG. 5 is a flowchart illustrating an exemplary method for efficientcalculation of both similarity search values and boundaries of digestblocks using a single linear calculation of rolling hash values in adata deduplication system in which aspects of the present invention maybe realized.

DETAILED DESCRIPTION OF THE DRAWINGS

Data deduplication is a highly important and vibrant field in computingstorage systems. Data deduplication refers to the reduction and/orelimination of redundant data. In data deduplication, a data object,which may be a file, a data stream, or some other form of data, isbroken down into one or more parts called chunks or blocks. In a datadeduplication process, duplicate copies of data are reduced oreliminated, leaving a minimal amount of redundant copies, or a singlecopy of the data, respectively. The goal of a data deduplication systemis to store a single copy of duplicated data, and the challenges inachieving this goal are efficiently finding the duplicate data patternsin a typically large repository, and storing the data patterns in astorage efficient deduplicated form. A significant challenge indeduplication storage systems is scaling to support very largerepositories of data. Such large repositories can reach sizes ofPetabytes (1 Petabyte=2⁵⁰ bytes) or more. Deduplication storage systemssupporting such repository sizes, must provide efficient processing forfinding duplicate data patterns within the repositories, whereefficiency is measured in resource consumption for achievingdeduplication (resources may be CPU cycles, RAM storage, persistentstorage, networking, etc.). In one embodiment, a deduplication storagesystem may be based on maintaining a search optimized index of valuesknown as fingerprints or digests, where a (small) fingerprint representsa (larger) block of data in the repository. The fingerprint values maybe cryptographic hash values calculated based on the blocks' data. Inone embodiment, secure hash algorithm (SHA), e.g. SHA-1 or SHA-256,which are a family of cryptographic hash functions, may be used.Identifying fingerprint matches, using index lookup, enables to storereferences to data that already exists in a repository.

To provide reasonable deduplication in this approach, the mean size ofthe data blocks based on which fingerprints are generated must belimited to smaller sizes and may not be too large. The reason being thata change of a bit within a data block will probabilistically change thedata block's corresponding fingerprint, and thus having large datablocks makes the scheme more sensitive to updates in the data ascompared to having small blocks. A typical data block size may rangefrom 4 KB to 64 KB, depending on the type of application and workload.Thus, by way of example only, small data blocks may range in sizes of upto 64 KB, and large data blocks are those data blocks having a sizelarger than 64 KB.

To support very large repositories scaling to Petabytes (e.g.,repositories scaling to at least one Petabyte), the number offingerprints to store coupled with the size of a fingerprint (rangingbetween 16 bytes and 64 bytes), becomes prohibitive. For example, for 1Petabyte of deduplicated data, with a 4 KB mean data block size, and 32bytes fingerprint size (e.g. of SHA-256), the storage required to storethe fingerprints is 8 Terabytes. Maintaining a search optimized datastructure for such volumes of fingerprints is difficult, and requiresoptimization techniques. However existing optimization techniques do notscale to these sizes while maintaining performance. For this reason, toprovide reasonable performance, the supported repositories have to berelatively small (on the order of tens of TB). Even for such smallersizes, considerable challenges and run-time costs arise due to the largescale of the fingerprint indexes that create a bottle-neck indeduplication processing.

To solve this problem, in one embodiment, a deduplication system may bebased on a two step approach for searching data patterns duringdeduplication. In the first step, a large chunk of incoming data (e.g. afew megabytes) is searched in the repository for similar (rather thanidentical) data chunks of existing data, and the incoming data chunk ispartitioned accordingly into intervals and paired with corresponding(similar) repository intervals. In the second step, a byte-wise matchingalgorithm is applied on pairs of similar intervals, to identifyidentical sub-intervals, which are already stored in a repository ofdata. The matching algorithm of the second step relies on reading allthe relevant similar data in the repository in order to compare itbyte-wise to the input data.

Yet, a problem stemming from a byte-wise comparison of data underlyingthe matching algorithm of the second step, is that data of roughly thesame size and rate as the incoming data should be read from therepository, for comparison purposes. For example, a system processing 1GB of incoming data per second, should read about 1 GB of data persecond from the repository for byte-wise comparison. This requiressubstantially high capacities of I/O per second of the storage devicesstoring the repository data, which in turn increases their cost.

Additional trends in information technology coinciding with the aboveproblem are the following: (1) Improvements in the computing ability byincreasing CPU speeds and the number of CPU cores. (2) Increase in diskdensity, while disk throughput remains relatively constant or improvingonly modestly. This means that there are fewer spindles relative to thedata capacity, thus practically reducing the overall throughput. Due tothe problem specified above, there is a need to design an alternativesolution, to be integrated in a two step deduplication system embodimentspecified above, that does not require reading from the repository inhigh rates/volumes.

Therefore, in one embodiment, by way of example only, additionalembodiments address these problem, as well as shifts resourceconsumption from disks to the CPUs, to benefit from the above trends.The embodiments described herein are integrated within the two step andscalable deduplication embodiments embodiment described above, and usesa similarity search to focus lookup of digests during deduplication. Inone embodiment, a global similarity search is used as a basis forfocusing the similarity search for digests of repository data that ismost likely to match input data.

The embodiments described herein significantly reduce the capacity ofI/O per second required of underlying disks, benefit from the increasesin computing ability and in disk density, and considerably reduce thecosts of processing, as well as maintenance costs and environmentaloverhead (e.g. power consumption).

In one embodiment, input data is segmented into small segments (e.g. 4KB) and a digest (a cryptographic hash value, e.g. SHA1) is calculatedfor each such segment. First, a similarity search algorithm, asdescribed above, is applied on an input chunk of data (e.g. 16 MB), andthe positions of the most similar reference data in the repository arelocated and found. These positions are then used to lookup the digestsof the similar reference data. The digests of all the data contained inthe repository are stored and retrieved in a form that corresponds totheir occurrence in the data. Given a position of a section of datacontained in the repository, the digests associated with the section ofdata are efficiently located in the repository and retrieved. Next,these reference digests are loaded into memory, and instead of comparingdata to find matches, the input digests and the loaded reference digestsare matched.

The described embodiments provide a new fundamental approach forarchitecting a data deduplication system, which integrates a scalabletwo step approach of similarity search followed by a search of identicalmatching segments, with an efficient and cost effectivedigest/fingerprint based matching algorithm (instead of byte-wise datacomparison). The digest/fingerprint based matching algorithm enables toread only a small fraction (1%) of the volume of data required bybyte-wise data comparison. The present invention proposed herein, adeduplication system can provide high scalability to very large datarepositories, in addition to high efficiency and performance, andreduced costs of processing and hardware.

In one embodiment, by way of example only, the term “similar data” maybe referred to as: for any given input data, data which is similar tothe input data is defined as data which is mostly the same (i.e. notentirely but at least 50% similar) as the input data. From looking atthe data in a binary view (perspective), this means that similar data isdata where most (i.e. not entirely but at least 50% similar) of thebytes are the same as the input data.

In one embodiment, by way of example only, the term “similar search” maybe referred to as the process of searching for data which is similar toinput data in a repository of data. In one embodiment, this process maybe performed using a search structure of similarity elements, which ismaintained and searched within.

In one embodiment, by way of example only, the term “similarityelements” may be calculated based on the data and facilitate a globalsearch for data which is similar to input data in a repository of data.In general, one or more similarity elements are calculated, andrepresent, a large (e.g. at least 16 MB) chunk of data.

Thus, the various embodiments described herein provide various solutionsfor digest retrieval based on a similarity search in deduplicationprocessing in a data deduplication system using a processor device in acomputing environment. In one embodiment, by way of example only, inputdata is partitioned into fixed sized data chunks. Similarity elementsdigest block boundaries and digest values are calculated for each of thefixed sized data chunks. Matching similarity elements are searched forin a search structure (i.e. index) containing the similarity elementsfor each of the fixed sized data chunks in a repository of data.Positions of similar data are located in a repository. The positions ofthe similar data are used to locate and load into the memory storeddigest values and corresponding stored digest block boundaries of thesimilar data in the repository. It should be noted that in oneembodiment the positions may be either physical or logical (i.e.virtual). The positions are of data inside a repository of data. Theimportant property of a ‘position’ is that given a position (physical orlogical) in the repository's data, the data in that position can beefficiently located and accessed. The digest values and thecorresponding digest block boundaries are matched with the stored digestvalues and the corresponding stored digest block boundaries to find datamatches.

Thus, the various embodiments described herein provide various solutionsfor digest retrieval based on a similarity search in deduplicationprocessing in a data deduplication system using a processor device in acomputing environment. In one embodiment, by way of example only, inputdata is partitioned into fixed sized data chunks. Similarity elements,digest block boundaries and digest values are calculated for each of thefixed sized data chunks. Matching similarity elements are searched forin a search structure (i.e. index) containing the similarity elementsfor each of the fixed sized data chunks in a repository of data.Positions of similar data are located in a repository. The positions ofthe similar data are used to locate and load into the memory storeddigest values and corresponding stored digest block boundaries of thesimilar data in the repository. The digest values and the correspondingdigest block boundaries are matched with the stored digest values andthe corresponding stored digest block boundaries to find data matches.

In one embodiment, the present invention provides a solution forutilizing a similarity search to load into memory the relevant digestsfrom the repository, for efficient deduplication processing. In a datadeduplication system, deduplication is performed by partitioning thedata into large fixed sized chunks, and for each chunk calculating (2things−similarity elements and digest blocks/digest values) hash values(digest block/digest value) for similarity search and digest values. Thedata deduplication system searches for matching similarity values of thechunks in a search structure of similarity values, and finds thepositions of similar data in the repository. The data deduplicationsystem uses these positions of similar data to locate and load intomemory stored digests of the similar repository data, and matching inputand repository digest values to find data matches.

In one embodiment, the present invention provides for efficientcalculation of both similarity search values and segmentation (i.e.boundaries) of digest blocks using a single linear calculation ofrolling hash values. In a data deduplication system, the input data ispartitioned into chunks, and for each chunk a set of rolling hash valuesis calculated. A single linear scan of the rolling hash values producesboth similarity search values and boundaries of the digest blocks of thechunk. Each rolling hash value corresponds to a consecutive window ofbytes in byte offsets. The similarity search values are used to searchfor similar data in the repository. The digest blocks segmentation isused to calculate digest block boundaries and corresponding digestvalues of the chunk, for digests matching. Each rolling hash valuecontributes to the calculation of the similarity values and to thecalculation of the digest blocks segmentations. Each rolling hash valuemay be discarded after contributing to the calculations. The describedembodiment provides significant processing efficiency and reduction ofCPU consumption, as well as considerable performance improvement.

Thus, as described above, the deduplication approach of the presentinvention uses a two-step process for searching data patterns duringdeduplication. In the first step, a large chunk of incoming data (e.g. 2megabytes “MB”) is searched in the repository for similar (rather thanidentical) chunks of existing data, and the incoming chunk ispartitioned accordingly into intervals, and paired with corresponding(similar) repository intervals. The similarity index used in the firststep is compact and simple to maintain and search within, because theelements used for a similarity search are very compact relative to thedata they represent (e.g. 16 bytes representing 4 megabytes). Furtherincluded in the first step, in addition to a calculation of similarityelements, is a calculation of digest segments and respective digestvalues for the input chunk of data. All these calculations are based ona single calculation of rolling hash values. In the second step,reference digests of the similar repository intervals are retrieved, andthen the input digests are matched with the reference digests, toidentify data matches.

In one embodiment, in the similarity based deduplication approach asdescribed herein, a stream of input data is partitioned into chunks(e.g. at least 16 MB), and each chunk is processed in two main steps. Inthe first step a similarity search process is applied, and positions ofthe most similar reference data in the repository are found. Within thisstep both similarity search elements and digest segments boundaries arecalculated for the input chunk, based on a single linear calculation ofrolling hash values. Digest values are calculated for the input chunkbased on the produced segmentation, and stored in memory in the sequenceof their occurrence in the input data. The positions of similar data arethen used to lookup the digests of the similar reference data and loadthese digests into memory, also in a sequential form. Then, the inputdigests are matched with the reference digests to form data matches.

When deduplication of an input chunk of data is complete, the inputchunk of data's associated digests are stored in the repository, toserve as reference digests for subsequent input data. The digests arestored in a linear form, which is independent of the deduplicated formby which the data these digests describe is stored, and in the sequenceof their occurrence in the data. This method of storage enablesefficient retrieval of sections of digests, independent of fragmentationcharacterizing deduplicated storage forms, and thus low on IO andcomputational resource consumption.

Turning now to FIG. 1, exemplary architecture 10 of a computing systemenvironment is depicted. The computer system 10 includes centralprocessing unit (CPU) 12, which is connected to communication port 18and memory device 16. The communication port 18 is in communication witha communication network 20. The communication network 20 and storagenetwork may be configured to be in communication with server (hosts) 24and storage systems, which may include storage devices 14. The storagesystems may include hard disk drive (HDD) devices, solid-state devices(SSD) etc., which may be configured in a redundant array of independentdisks (RAID). The operations as described below may be executed onstorage device(s) 14, located in system 10 or elsewhere and may havemultiple memory devices 16 working independently and/or in conjunctionwith other CPU devices 12. Memory device 16 may include such memory aselectrically erasable programmable read only memory (EEPROM) or a hostof related devices. Memory device 16 and storage devices 14 areconnected to CPU 12 via a signal-bearing medium. In addition, CPU 12 isconnected through communication port 18 to a communication network 20,having an attached plurality of additional computer host systems 24. Inaddition, memory device 16 and the CPU 12 may be embedded and includedin each component of the computing system 10. Each storage system mayalso include separate and/or distinct memory devices 16 and CPU 12 thatwork in conjunction or as a separate memory device 16 and/or CPU 12.

FIG. 2 is an exemplary block diagram 200 showing a hardware structure ofa data storage system in a computer system according to the presentinvention. Host computers 210, 220, 225, are shown, each acting as acentral processing unit for performing data processing as part of a datastorage system 200. The cluster hosts/nodes (physical or virtualdevices), 210, 220, and 225 may be one or more new physical devices orlogical devices to accomplish the purposes of the present invention inthe data storage system 200. In one embodiment, by way of example only,a data storage system 200 may be implemented as IBM® ProtecTIER®deduplication system TS7650G™. A Network connection 260 may be a fibrechannel fabric, a fibre channel point to point link, a fibre channelover ethernet fabric or point to point link, a FICON or ESCON I/Ointerface, any other I/O interface type, a wireless network, a wirednetwork, a LAN, a WAN, heterogeneous, homogeneous, public (i.e. theInternet), private, or any combination thereof. The hosts, 210, 220, and225 may be local or distributed among one or more locations and may beequipped with any type of fabric (or fabric channel) (not shown in FIG.2) or network adapter 260 to the storage controller 240, such as Fibrechannel, FICON, ESCON, Ethernet, fiber optic, wireless, or coaxialadapters. Data storage system 200 is accordingly equipped with asuitable fabric (not shown in FIG. 2) or network adaptor 260 tocommunicate. Data storage system 200 is depicted in FIG. 2 comprisingstorage controllers 240 and cluster hosts 210, 220, and 225. The clusterhosts 210, 220, and 225 may include cluster nodes.

To facilitate a clearer understanding of the methods described herein,storage controller 240 is shown in FIG. 2 as a single processing unit,including a microprocessor 242, system memory 243 and nonvolatilestorage (“NVS”) 216. It is noted that in some embodiments, storagecontroller 240 is comprised of multiple processing units, each withtheir own processor complex and system memory, and interconnected by adedicated network within data storage system 200. Storage 230 (labeledas 230 a, 230 b, and 230 n in FIG. 3) may be comprised of one or morestorage devices, such as storage arrays, which are connected to storagecontroller 240 (by a storage network) with one or more cluster hosts210, 220, and 225 connected to each storage controller 240.

In some embodiments, the devices included in storage 230 may beconnected in a loop architecture. Storage controller 240 manages storage230 and facilitates the processing of write and read requests intendedfor storage 230. The system memory 243 of storage controller 240 storesprogram instructions and data, which the processor 242 may access forexecuting functions and method steps of the present invention forexecuting and managing storage 230 as described herein. In oneembodiment, system memory 243 includes, is in association with, or is incommunication with the operation software 250 for performing methods andoperations described herein. As shown in FIG. 2, system memory 243 mayalso include or be in communication with a cache 245 for storage 230,also referred to herein as a “cache memory”, for buffering “write data”and “read data”, which respectively refer to write/read requests andtheir associated data. In one embodiment, cache 245 is allocated in adevice external to system memory 243, yet remains accessible bymicroprocessor 242 and may serve to provide additional security againstdata loss, in addition to carrying out the operations as described inherein.

In some embodiments, cache 245 is implemented with a volatile memory andnon-volatile memory and coupled to microprocessor 242 via a local bus(not shown in FIG. 2) for enhanced performance of data storage system200. The NVS 216 included in data storage controller is accessible bymicroprocessor 242 and serves to provide additional support foroperations and execution of the present invention as described in otherfigures. The NVS 216, may also referred to as a “persistent” cache, or“cache memory” and is implemented with nonvolatile memory that may ormay not utilize external power to retain data stored therein. The NVSmay be stored in and with the cache 245 for any purposes suited toaccomplish the objectives of the present invention. In some embodiments,a backup power source (not shown in FIG. 2), such as a battery, suppliesNVS 216 with sufficient power to retain the data stored therein in caseof power loss to data storage system 200. In certain embodiments, thecapacity of NVS 216 is less than or equal to the total capacity of cache245.

Storage 230 may be physically comprised of one or more storage devices,such as storage arrays. A storage array is a logical grouping ofindividual storage devices, such as a hard disk. In certain embodiments,storage 230 is comprised of a JBOD (Just a Bunch of Disks) array or aRAID (Redundant Array of Independent Disks) array. A collection ofphysical storage arrays may be further combined to form a rank, whichdissociates the physical storage from the logical configuration. Thestorage space in a rank may be allocated into logical volumes, whichdefine the storage location specified in a write/read request.

In one embodiment, by way of example only, the storage system as shownin FIG. 2 may include a logical volume, or simply “volume,” may havedifferent kinds of allocations. Storage 230 a, 230 b and 230 n are shownas ranks in data storage system 200, and are referred to herein as rank230 a, 230 b and 230 n. Ranks may be local to data storage system 200,or may be located at a physically remote location. In other words, alocal storage controller may connect with a remote storage controllerand manage storage at the remote location. Rank 230 a is shownconfigured with two entire volumes, 234 and 236, as well as one partialvolume 232 a. Rank 230 b is shown with another partial volume 232 b.Thus volume 232 is allocated across ranks 230 a and 230 b. Rank 230 n isshown as being fully allocated to volume 238—that is, rank 230 n refersto the entire physical storage for volume 238. From the above examples,it will be appreciated that a rank may be configured to include one ormore partial and/or entire volumes. Volumes and ranks may further bedivided into so-called “tracks,” which represent a fixed block ofstorage. A track is therefore associated with a given volume and may begiven a given rank.

The storage controller 240 may include a data duplication module 255, asimilarity index module 257 (e.g., a similarity search structure), and asimilarity search module 259. The data duplication module 255, thesimilarity index module 257, and the similarity search module 259 maywork in conjunction with each and every component of the storagecontroller 240, the hosts 210, 220, 225, and storage devices 230. Thedata duplication module 255, the similarity index module 257, and thesimilarity search module 259 may be structurally one complete module ormay be associated and/or included with other individual modules. Thedata duplication module 255, the similarity index module 257, and thesimilarity search module 259 may also be located in the cache 245 orother components.

The storage controller 240 includes a control switch 241 for controllingthe fiber channel protocol to the host computers 210, 220, 225, amicroprocessor 242 for controlling all the storage controller 240, anonvolatile control memory 243 for storing a microprogram (operationsoftware) 250 for controlling the operation of storage controller 240,data for control, cache 245 for temporarily storing (buffering) data,and buffers 244 for assisting the cache 245 to read and write data, acontrol switch 241 for controlling a protocol to control data transferto or from the storage devices 230, the data duplication module 255, thesimilarity index module 257, and the similarity search module 259, inwhich information may be set. Multiple buffers 244 may be implementedwith the present invention to assist with the operations as describedherein. In one embodiment, the cluster hosts/nodes, 210, 220, 225 andthe storage controller 240 are connected through a network adaptor (thiscould be a fibre channel) 260 as an interface i.e., via at least oneswitch called “fabric.”

In one embodiment, the host computers or one or more physical or virtualdevices, 210, 220, 225 and the storage controller 240 are connectedthrough a network (this could be a fibre channel) 260 as an interfacei.e., via at least one switch called “fabric.” In one embodiment, theoperation of the system shown in FIG. 2 will be described. Themicroprocessor 242 may control the memory 243 to store commandinformation from the host device (physical or virtual) 210 andinformation for identifying the host device (physical or virtual) 210.The control switch 241, the buffers 244, the cache 245, the operatingsoftware 250, the microprocessor 242, memory 243, NVS 216, dataduplication module 255, the similarity index module 257, and thesimilarity search module 259 are in communication with each other andmay be separate or one individual component(s). Also, several, if notall of the components, such as the operation software 250 may beincluded with the memory 243. Each of the components within the devicesshown may be linked together and may be in communication with each otherfor purposes suited to the present invention. As mentioned above, thedata duplication module 255, the similarity index module 257, and thesimilarity search module 259 may also be located in the cache 245 orother components. As such, the data duplication module 255, thesimilarity index module 257, and the similarity search module 259 maybeused as needed, based upon the storage architecture and userspreferences.

As mentioned above, in one embodiment, the input data is partitionedinto large fixed size chunks (e.g. 16 MB), and a similarity searchprocedure is applied for each input chunk. A similarity search procedurecalculates compact similarity elements, which may also be referred to asdistinguishing characteristics (DCs), based on the input chunk of data,and searches for matching similarity elements stored in a compact searchstructure (i.e. index) in the repository. The size of the similarityelements stored per each chunk of data is typically 32 bytes (where thechunk size is a few megabytes), thus making the search structure storingthe similarity elements very compact and simple to maintain and searchwithin.

The similarity elements are calculated by calculating rolling hashvalues on the chunk's data, namely producing a rolling hash value foreach consecutive window of bytes in a byte offset, and then selectingspecific hash values and associated positions (not necessarily the exactpositions of these hash values) to be the similarity elements of thechunk.

One important aspect and novelty provided by the present invention isthat a single linear calculation of rolling hash values, which is acomputationally expensive operation, serves as basis for calculatingboth the similarity elements of a chunk (for a similarity search) andthe segmentation of the chunk's data into digest blocks (for findingexact matches). Each rolling hash value is added to the calculation ofthe similarity elements as well as to the calculation of the digestblocks segmentation. After being added to the two calculations, arolling hash value can be discarded, as the need to store the rollinghash values is minimized or eliminated. This algorithmic elementprovides significant efficiency and reduction of CPU consumption, aswell as considerable performance improvement.

In one embodiment, the similarity search procedure of the presentinvention produces two types of output. The first type of output is aset of positions of the most similar reference data in the repository.The second type of output is the digests of the input chunk, comprisingof the segmentation to digest blocks and the digest values correspondingto the digest blocks, where the digest values are calculated based onthe data of the digest blocks.

In one embodiment, the digests are stored in the repository in a formthat corresponds to the digests occurrence in the data. Given a positionin the repository and size of a section of data, the location in therepository of the digests corresponding to that interval of data isefficiently determined. The positions produced by the similarity searchprocedure are then used to lookup the stored digests of the similarreference data, and to load these reference digests into memory. Then,rather than comparing data, the input digests and the loaded referencedigests are matched. The matching process is performed by loading thereference digests into a compact search structure of digests in memory,and then for each input digest, querying the search structure of digestsfor existence of that digest value. Search in the search structure ofdigests is performed based on the digest values. If a match is found,then the input data associated with that digest is determined to befound in the repository and the position of the input data in therepository is determined based on the reference digest's position in therepository. In this case, the identity between the input data covered bythe input digest, and the repository data covered by the matchingreference digest, is recorded. If a match is not found then the inputdata associated with that digest is determined to be not found in therepository, and is recorded as new data. In one embodiment, thesimilarity search structure is a global search structure of similarityelements, and a memory search structure of digests' is a local searchstructure of digests in memory. The search in the memory searchstructure of digests is performed by the digest values.

FIG. 3 is a flowchart illustrating an exemplary method 300 for digestretrieval based on similarity search in deduplication processing in adata deduplication system in which aspects of the present invention maybe realized. The method 300 begins (step 302). The method 300 partitionsinput data into data chunks (step 304). The input data may bepartitioned into fixed sized data chunks. The method 300 calculates, foreach of the data chunks, similarity elements, digest block boundaries,and corresponding digest values are calculated (step 306). The method300 searches for matching similarity elements in a search structure(i.e. index) for each of the data chunks (which may be fixed size datachunks) (step 308). The positions of the similar data in a repository(e.g., a repository of data) are located (step 310). The method 300 usesthe positions of the similar data to locate and load into memory storeddigest values and corresponding stored digest block boundaries of thesimilar data in the repository (step 312). The method 300 matches thedigest values and the corresponding digest block boundaries of the inputdata with the stored digest values and the corresponding stored digestblock boundaries to find data matches (step 314). The method 300 ends(step 316).

FIG. 4 is a flowchart illustrating an exemplary alternative method 400for digest retrieval based on similarity search in deduplicationprocessing in a data deduplication system in which aspects of thepresent invention may be realized. The method 400 begins (step 402). Themethod 400 partitions the input data into chunks (e.g., partitions theinput data into large fixed size chunks) (step 404), and for an inputdata chunk calculates rolling hash values, similarity elements, digestblock boundaries, and digest values based on data of the input datachunk (step 406). The method 400 searches for similarity elements of theinput data chunk in a similarity search structure (i.e. index) (step 408and 410). The method 400 determines if there are enough or a sufficientamount of matching similarity elements (step 412). If not enoughmatching similarity elements are found then the method 400 determinesthat no similar data is found in the repository for the input datachunk, and the data of the input chunk is stored in a repository (step414) and then the method 400 ends (step 438). If enough similarityelements are found, then for each similar data interval found in arepository, the method 400 determines the position and size of eachsimilar data interval in the repository (step 416). The method 400locates the digests representing the similar data interval in therepository (step 418). The method 400 loads these digests into a searchdata structure of digests in memory (step 420). The method 400determines if there are any additional similar data intervals (step422). If yes, the method 400 returns to step 416. If no, the method 400considers each digest of the input data chunk (step 424). The method 400determines if the digest value exists in the memory search structure ofdigests (step 426). If yes, the method 400 records the identity betweenthe input data covered by the digest and the repository data having thematching digest value (step 428). If no, the method 400 records that theinput data covered by the digest is not found in the repository (step430). From both steps 428 and 430, the method 400 determines if thereare additional digests of the input data chunk (step 432). If yes, themethod 400 returns to step 424. If no, method 400 removes the similarityelements of the matched data in the repository from the similaritysearch structure (step 434 and step 410). The method 400 adds thesimilarity elements of the input data chunk to the similarity searchstructure (step 436). The method 400 ends (step 438).

FIG. 5 is a flowchart illustrating an exemplary method 500 for efficientcalculation of both similarity search values and boundaries of digestblocks using a single linear calculation of rolling hash values in adata deduplication system in which aspects of the present invention maybe realized. The method 500 begins (step 502). The method 500 partitionsinput data into data chunks (steps 504). The data chunks may be fixedsized data chunks. The method 500 considers each consecutive window ofbytes in a byte offset in the input data (step 506). The method 500determines if there is an additional consecutive window of bytes to beprocessed (step 508). If yes, the method 500 calculates a rolling hashvalue based on the data of the consecutive window of bytes (step 510).The method 500 contributes the rolling hash value to the calculation ofthe similarity values and to the calculation of the digest blockssegmentations (i.e., the digest block boundaries) (step 512). The method500 discards the rolling hash value (step 514), and returns to step 506.If no, the method 500 concludes the calculation of the similarityelements and of the digest blocks segmentation, producing the finalsimilarity elements and digest blocks segmentation of the input data(step 516). The method 500 calculates digest values based on the digestblocks segmentation, wherein each digest block is assigned with acorresponding digest value (step 518). The similarity elements are usedto search for similar data in the repository (step 520). The digestblocks and corresponding digest values are used for matching with digestblocks and corresponding digest values stored in a repository fordetermining data in the repository which is identical to the input data(step 522). The method 500 ends (step 524).

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that may contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wired, optical fiber cable, RF, etc., or any suitable combination of theforegoing. Computer program code for carrying out operations for aspectsof the present invention may be written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Java, Smalltalk, C++ or the like and conventionalprocedural programming languages, such as the “C” programming languageor similar programming languages. The program code may execute entirelyon the user's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention have been described above withreference to flowchart illustrations and/or block diagrams of methods,apparatus (systems) and computer program products according toembodiments of the invention. It will be understood that each block ofthe flowchart illustrations and/or block diagrams, and combinations ofblocks in the flowchart illustrations and/or block diagrams, may beimplemented by computer program instructions. These computer programinstructions may be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

These computer program instructions may also be stored in a computerreadable medium that may direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks. The computer program instructions may also beloaded onto a computer, other programmable data processing apparatus, orother devices to cause a series of operational steps to be performed onthe computer, other programmable apparatus or other devices to produce acomputer implemented process such that the instructions which execute onthe computer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowchart and block diagrams in the above figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, may be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

What is claimed is:
 1. A method for efficient calculation of bothsimilarity search values and boundaries of digest blocks in a datadeduplication system using a processor device in a computingenvironment, comprising: partitioning input data into data chunks;calculating a set of rolling hash values for each of the data chunks;using a single linear scan of the rolling hash values for producing boththe similarity search values and the boundaries of the digest blocks;and using each of the rolling hash values to contribute to thecalculation of the similarity search values and to the calculation ofthe boundaries of the digest blocks.
 2. The method of claim 1, furtherincluding discarding each of the rolling hash values after contributingto the calculation of the similarity search values and to thecalculation of the boundaries of the digest blocks.
 3. The method ofclaim 1, further including corresponding each of the rolling hash valuesto a consecutive window of bytes in byte offsets.
 4. The method of claim1, further including using the similarity search values to search fordata similar to the input data in a repository of data.
 5. The method ofclaim 1, further including using the boundaries of the digest blocks tocalculate digest values for each of the data chunks for digestsmatching.
 6. The method of claim 1, further including partitioning theinput data into fixed sized data chunks.
 7. A system for efficientcalculation of both similarity search values and boundaries of digestblocks in a data deduplication system of a computing environment, thesystem comprising: the data deduplication system; a repository in thecomputing environment in communication with the data deduplicationsystem; at least one processor device operable in the computing storageenvironment for controlling the data deduplication system, wherein theat least one processor device: partitions input data into data chunks,calculates a set of rolling hash values for each of the data chunks,uses a single linear scan of the rolling hash values for producing boththe similarity search values and the boundaries of the digest blocks,and uses each of the rolling hash values to contribute to thecalculation of the similarity search values and to the calculation ofthe boundaries of the digest blocks.
 8. The system of claim 7, whereinthe at least one processor device discards each of the rolling hashvalues after contributing to the calculation of the similarity searchvalues and to the calculation of the boundaries of the digest blocks. 9.The system of claim 7, wherein the at least one processor devicecorresponds each of the rolling hash values to a consecutive window ofbytes in byte offsets.
 10. The system of claim 7, wherein the at leastone processor device uses the similarity search values to search fordata similar to the input data in the repository of data.
 11. The systemof claim 7, wherein the at least one processor device uses theboundaries of the digest blocks to calculate digest values for each ofthe data chunks for digests matching.
 12. The system of claim 7, whereinthe at least one processor device the input data into fixed sized datachunks.
 13. A computer program product for efficient calculation of bothsimilarity search values and boundaries of digest blocks in a datadeduplication system using a processor device in a computingenvironment, the computer program product comprising a computer-readablestorage medium having computer-readable program code portions storedtherein, the computer-readable program code portions comprising: anexecutable portion that partitions input data into data chunks; anexecutable portion that calculates a set of rolling hash values for eachof the data chunks; an executable portion that uses a single linear scanof the rolling hash values for producing both the similarity searchvalues and the boundaries of the digest blocks; and an executableportion that uses each of the rolling hash values to contribute to thecalculation of the similarity search values and to the calculation ofthe boundaries of the digest blocks.
 14. The computer program product ofclaim 13, further including an executable portion that discards each ofthe rolling hash values after contributing to the calculation of thesimilarity search values and to the calculation of the boundaries of thedigest blocks.
 15. The computer program product of claim 13, furtherincluding an executable portion that corresponds each of the rollinghash values to a consecutive window of bytes in byte offsets.
 16. Thecomputer program product of claim 13, further including an executableportion that uses the similarity search values to search for datasimilar to the input data in a repository of data.
 17. The computerprogram product of claim 13, further including an executable portionthat uses the boundaries of the digest blocks to calculate digest valuesfor each of the data chunks for digests matching.
 18. The computerprogram product of claim 13, further including an executable portionthat partitions the input data into fixed sized data chunks.